<p>The following parameters control the duration of the annealing algorithm:</p>

<table>
<tr><td>in GUI</td><td>in CLI</td><td>Description</td></tr>
<tr><td>Number of annealing stages</td><td>sa_num_anneal_stages</td><td>The annealing algorithm will run for this many number of stages.</td></tr>
<tr><td>Iterations per stage</td><td>sa_iters_per_stage</td><td>At each stage, the annealing algorithm will propose at least this many new parameter configurations.</td></tr>
<tr><td>Set-back interval size</td><td>sa_set_back</td><td>Upon accepting a higher-likelihood proposal, the annealing algorithm will consider this many additional iterations at this stage.</td></tr>
<tr><td>Starting temperature</td><td>sa_temp_start</td><td>The annealing algorithm begins with this temperature.</td></tr>
<tr><td>Final temperature</td><td>sa_temp_end</td><td>The annealing algorithm ends when this temperature is reached.</td></tr>
<tr><td>in GUI</td><td>sa_scale_temp</td><td>If this value equals 'yes' or 1, then the starting temperature will be automatically scaled based on 100 random proposition samples.  In theory, the new scaled starting temperature will better reflect the approximal ruggedness of the landscape.  The new scaled starting temperature will equal S * sqrt(fsqsum - fsum * fsum) / T, where S is a constant scaler (0.3), fsqsum equals the mean squared likelihood of the initial 100 proposals, fsum equals the squared mean likelihood of the initial 100 proposals, and T is the initial user-specified starting temperature.</td></tr>
<tr><td>in GUI</td><td>sa_preopt</td><td>if this value equals 'yes' or 1, then the annealing algorithm will first optimize all free parameters using PhyML's standard hill-climbing strategy.  Annealing will begin from where the hill-climbing stopped.  It is recommended that you enable this option.</td></tr>
</table>

<hr>
<p>The proposition parameters control how often each parameter is stochastically purturbed during the annealing algorithm.  All of these values can vary from 0.0 (in which case the parameter is never purturbed) to 1.0 (in which case the parameter is purturbed at every iteration).</p>

<table>
<tr><td>Probability of proposing topology</td><td>sa_prob_topo</td><td></td></tr>
<tr><td>Probability of using NNI proposition</td><td>sa_prob_NNI</td><td>The probability of using nearest-neighbor interchange to propose the new topology. This probability is contingent on sa_prob_topo.  The probability of purturbing the topology AND using NNI to make the proposition equals (sa_prob_topo X sa_prob_NNI).</td></tr>
<tr><td>Probability of using SPR proposition</td><td>sa_prob_SPR</td><td>THe probability of using subtree pruning and regrafting to propose the new topology. This probability is contingent on sa_prob_topo.  The probability of purturbing the topology AND using SPR to make the proposition equals (sa_prob_topo X sa_prob_SPR).</td></tr>
<tr><td>Probability of proposing branch lengths</td><td>sa_prob_brlen</td><td></td></tr>
<tr><td>Probability of proposing alpha value (for +G)</td><td>sa_prob_gamma</td><td></td></tr>
<tr><td>Probability of proposing kappa</td><td>sa_prob_kappa</td><td></td></tr>
<tr><td>Probability of proposing lambda</td><td>sa_prob_lambda</td><td></td></tr>
<tr><td>Probability of proposing relative sub. rates</td><td>sa_prob_rr</td><td></td></tr>
<tr><td>Probability of stepping state frequencies</td><td>sa_prob_pi</td><td></td></tr>
<tr><td>Probability of proposing mixture props (for +B)</td><td>sa_prob_blprop</td><td></td></tr>
<tr><td>Probability of proposing P-invar value (for +I)</td><td>sa_prob_pinvar</td><td></td></tr>
</table>
<hr>

<p>The proposal strategy parameters control the types of distributions from random values are drawn. The valid values are: 0 to optimize this parameter with quasi-Newton methods rather than random perturbations. 1 to draw from a Gaussian distribution. 2 to draw from a Dirichlet distribution (not applicable to all parameters).</p>

<table>
<tr><td>(not in GUI)</td><td>sa_sele_max_params</td><td>Description</td></tr>
<tr><td>Branch length proposal strategy</td><td>sa_sele_bl</td><td>Description</td></tr>
<tr><td>Mixture proportion proposal strategy</td><td>sa_sele_blprop</td><td>Description</td></tr>
<tr><td>Alpha proposal strategy</td><td>sa_sele_gamma</td><td>Description</td></tr>
<tr><td>Kappa proposal strategy</td><td>sa_sele_kappa</td><td>Description</td></tr>
<tr><td>Lambda proposal strategy</td><td>sa_sele_lambda</td><td>Description</td></tr>
<tr><td>State frequency proposal strategy</td><td>sa_sele_pi</td><td>Description</td></tr>
<tr><td>P-invar proposal strategy</td><td>sa_sele_pinvar</td><td>Description</td></tr>
<tr><td>GTR relative rate proposal strategy</td><td>sa_sele_rr</td><td>Description</td></tr>
</table>

<p>The sigma parameters control the shape of the underlying Gaussian distribution, valid for parameters whose proposal strategy is to use a Gaussian distribution.  The values can range from 0.0 to +infinity.  In general, small sigma values encourage smaller proposition steps, while large sigma values encourage large proposition steps.</p>

<table>
<tr><td>in GUI</td><td>sa_sigma_bl</td><td>branch lengths</td></tr>
<tr><td>in GUI</td><td>sa_sigma_blprop</td><td>branch length mixture model proportions</td></tr>
<tr><td>in GUI</td><td>sa_sigma_pinvar</td><td>proportion invariant (for +I)</td></tr>
<tr><td>in GUI</td><td>sa_sigma_gamma</td><td>alpha, for the discrete gamma distributed rates model (for +G)</td></tr>
<tr><td>in GUI</td><td>sa_sigma_lambda</td><td>lambda</td></tr>
<tr><td>in GUI</td><td>sa_sigma_kappa</td><td>kappa</td></tr>
<tr><td>in GUI</td><td>sa_sigma_pi</td><td>the `pi' vector of equilibrium state frequencies</td></tr>
<tr><td>in GUI</td><td>sa_sigma_rr</td><td>the relataive substitution rates for the GTR model</td></tr>
</table>